State of AI

Week 1, July 2023

Greetings,

Welcome to the 13th edition of the State of AI. This issue will take you on a journey through some of the most exciting and innovative AI advancements. We commence by exploring the synthesis of high-quality images from brain EEG signals, unlocking an entirely new approach to visual creation. Our journey continues through the intellectual realm of theorem proving, where retrieval-augmented language models are setting new standards.

We then anchor in the world of grounded multimodal language models, pushing the boundaries of AI-world interaction. Our exploration doesn't stop there; we delve into the exciting capabilities of generating any desired object in any scene, reshaping our perceptions of AI's creative potential. Finally, we delve into the educational sphere, benchmarking the performance of prominent AI models and human tutors in programming education.

Each topic offers a distinct insight into the ever-evolving world of AI, promising a captivating read. Enjoy!

Best regards,

State of AI

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Contents

1. DreamDiffusion: Generating High-Quality Images from Brain EEG Signals

2. LeanDojo: Theorem Proving with Retrieval-Augmented Language Models

3. Kosmos-2: Grounding Multimodal Large Language Models to the World

4. Generate Anything Anywhere in Any Scene Generative AI for Programming

5. Education: Benchmarking ChatGPT, GPT-4, and Human Tutors

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DreamDiffusion: Generating High-Quality Images from Brain EEG Signals

Authors: Yunpeng Bai, Xintao Wang, Yan-Pei Cao, Yixiao Ge, Chun Yuan, Ying Shan

Source & References: https://arxiv.org/abs/2306.16934v2

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The Idea of DreamDiffusion

Imagine being able to create images directly from your thoughts without the need to describe them in words. That's the goal of DreamDiffusion, a novel machine learning model proposed by a team of researchers from Tsinghua Shenzhen International Graduate School, Tencent AI Lab, and Peng Cheng Laboratory. The model generates high-quality images by leveraging brain electroencephalogram (EEG) signals, making it a significant step towards portable and low-cost "thoughts-to-image" technology.

Noisy EEG Signals

EEG signals are inherently noisy, and obtaining accurate and reliable information from them can be challenging. So, how can we create effective and robust EEG representations?

The researchers propose to pre-train an EEG encoder with masked signal modeling techniques to learn contextual knowledge from large-scale noisy EEG data. By randomly masking segments of the signals and reconstructing them in the time domain, the pre-trained encoder can better understand and represent EEG data across different individuals and brain activities.

A Powerful Text-to-Image Model

DreamDiffusion takes advantage of an existing powerful text-to-image model called Stable Diffusion (SD). SD is augmented with a cross-attention mechanism, providing flexible control over the generated images. When fine-tuning the model with limited EEG-image pairs, the researchers optimize the EEG encoder and cross-attention heads of the U-Net (used in SD) while keeping the other parts of the model fixed.

Aligning EEG, Text, and Image Spaces with CLIP

Since SD is specifically designed for text-to-image generation, its latent space is different from that of EEG signals. To align the spaces, the researchers employ additional supervision from the widely-known CLIP model. This step further optimizes the EEG embeddings by minimizing the distance between EEG and image embeddings obtained from the CLIP image encoder. As a result, refined EEG feature embeddings better align with CLIP image and text embeddings, improving the quality of generated images.

Results and Analysis

Evaluating the effectiveness and quality of the generated images, DreamDiffusion demonstrates impressive results, generating high-quality and realistic images from EEG signals alone. In comparison to existing methods, such as the Brain2Image model, DreamDiffusion produces images with significantly higher quality and accurate alignment to the EEG data.

Potential Applications and Limitations

DreamDiffusion has a broad range of potential applications. It offers an efficient and creative way to generate artistic content, capture fleeting inspirations, and visualize dreams or memories. The technology could also have therapeutic benefits for children with autism or those with language disabilities, as well as advancing research in neuroscience and computer vision.

However, there are some limitations to consider. The quality of the generated images relies heavily on the quality and amount of available EEG data. In addition, working with noisy or limited data can make aligning the EEG, text, and image spaces challenging. Continued research and development could help address these limitations and explore further applications.

A Step Forward for Brain-Computer Interfaces

Overall, DreamDiffusion represents an exciting step forward for brain-computer interfaces and the field of neuroscience. By generating high-quality images directly from brain EEG signals, the model opens up new avenues for research, therapy, and even artistic creation. The researchers have made their code publicly available, which can be found at https://github.com/bbaaii/DreamDiffusion, inviting further exploration and development.

In conclusion, the DreamDiffusion model shows great promise in generating realistic images directly from EEG signals and could potentially revolutionize the way we interact with technology. As research in this field continues to advance, we might see a future where our thoughts can be instantly visualized and brought to life in impressive detail.

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LeanDojo: Theorem Proving with Retrieval-Augmented Language Models

Authors: Kaiyu Yang, Aidan M. Swope, Alex Gu, Rahul Chalamala, Peiyang Song, Shixing Yu, Saad Godil, Ryan Prenger, Anima Anandkumar

Source & References: https://arxiv.org/abs/2306.15626

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Introduction

The world of AI has made tremendous strides in recent years, with Large Language Models (LLMs) demonstrating immense potential in proving formal theorems by interacting with proof assistants like Lean. Despite this promise, existing methods tend to be difficult to reproduce or build upon, due to barriers such as private code, data, and substantial compute requirements. The paper "LeanDojo: Theorem Proving with Retrieval-Augmented Language Models" aims to address these obstacles and facilitate further research in machine learning for theorem proving.